device='cuda')
model.load_weights('./snapshots/base.pth', load_opt=False)
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
train_datagen = DirectorySiameseLoader(
impath,
transforms.Compose([
transforms.Resize(256),
transforms.RandomResizedCrop(224),
transforms.RandomHorizontalFlip(),
transforms.RandomVerticalFlip(),
transforms.ToTensor(), normalize
]))
train_generator = train_datagen.get_dset(8, 1)
os.makedirs(f'./snapshots/pairs/{save_no}', exist_ok=True)
try:
h = model.fit_generator(
train_generator,
20,
schedule=[10, 15],
tensorboard=f'logs/pair/{len(os.listdir("logs/pair"))}',
epoch_end=model.checkpoint(f'./snapshots/pairs/{save_no}',
'ContrastiveLoss'),
step=200)
with open('siamese.json', 'w') as wr:
json.dump(h, wr)
finally:
model.save_weights('./snapshots/pairs_temp.pth')
transforms.Normalize((0.4914, 0.4822, 0.4465),
(0.2023, 0.1994, 0.2010)),
])
transform_test = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465),
(0.2023, 0.1994, 0.2010)),
])
trainset = CIFAR10(root=root,
train=True,
download=True,
transform=transform_train)
trainloader = Loader(trainset, batch_size=64, shuffle=True, num_workers=0)
testset = CIFAR10(root=root,
train=False,
download=True,
transform=transform_test)
testloader = Loader(testset, batch_size=100, shuffle=False, num_workers=0)
schedule = LambdaLR(sgd, lrstep)
history = model.fit_generator(trainloader,
300,
validation_data=testloader,
schedule=schedule)
with open('logs/misc-01.json', 'w') as wr:
json.dump(history, wr)